The resulting values of the clustering coefficient for our networks are all \(C \simeq 0.1\) . In previous works, [1]} found the clustering coefficient for a binary version of the WTW to be \(C=0.65\) ; however in their analysis they had limited data about only the forty most exchanged merchandises, thus truncating the network connectivity and biasing upward their result. In [2]} \(C\)  is found to be varying from \(\sim 0.001\)  to \(\sim 0.8\) , depending if one considered the binary or weighted version of the network respectively. For the IMN, [3]} found \(C=0.13 \sim 0.15\) . Our results, shown in Table REF , seems to be compatible with these previous works, once accounted for the different strategies adopted.Still, we have to recall that here we applied the hypergeometric filtering, hence probably biasing downward our results: as noted in [4]}, clustering coefficient value decreases controlling for the hypergeometric benchmark, indicating that some of the triangles observed in the network show weak ties which do not stand up to the hypergeometric test.
Nevertheless, the loss of a small part of the clustering structure does not spoil the current analysis, which aim is not to study the detailed structures of the two networks [5]}, [2]}, [7]} but instead to determine the interplay between trade and migration, taking advantage of their main topological properties. In this sense, the hypergeometric filtering let us obtain a binary and more tractable version of the two networks, making them more easily comparable, yet exploiting all the information available in the data.
